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Title

Novel Network Intrusion Detection System using Hybrid Neural Network (Hopfield and Kohonen SOM with Conscience Function)

Author

Wesam K. AL-Rashdan, Reyadh Naoum, Wafa' S. Al_Sharafat, Mu'taz Kh. Al-Khazaaleh

Citation

Vol. 10  No. 11  pp. 10-13

Abstract

Intrusion detection technology is an effective approach to dealing with the problems of network security. In this paper, it presents an intrusion detection model based on hybrid neural network and SVM. The key idea is to aim at taking advantage of classification abilities of neural network for unknown attacks and the expert-based system for the known attacks. We employ data from the third international knowledge discovery and data mining tools competition (KDDcup’99) to train and test the feasibility of our proposed neural network component. According to the results of our experiment, our model achieves 97.2 percent detection rate for DOS and Probing intrusions, and less than 0.04 percent false alarm rate. Expert system can detect R2L and U2R intrusions more accurately than neural network. Therefore, Hybrid model will improve the performance to detect intrusions.

Keywords

Novel Network, IDS, SOM, Hybrid Neural Network

URL

http://paper.ijcsns.org/07_book/201011/20101103.pdf